Three genes expressed in relation to lipid metabolism considered as potential biomarkers for the diagnosis and treatment of diabetic peripheral neuropathy

Diabetic neuropathy is one of the most common chronic complications and is present in approximately 50% of diabetic patients. A bioinformatic approach was used to analyze candidate genes involved in diabetic distal symmetric polyneuropathy and their potential mechanisms. GSE95849 was downloaded from the Gene Expression Omnibus database for differential analysis, together with the identified diabetic peripheral neuropathy-associated genes and the three major metabolism-associated genes in the CTD database to obtain overlapping Differentially Expressed Genes (DEGs). Gene Set Enrichment Analysis and Functional Enrichment Analysis were performed. Protein–Protein Interaction and hub gene networks were constructed using the STRING database and Cytoscape software. The expression levels of target genes were evaluated using GSE24290 samples, followed by Receiver operating characteristic, curve analysis. And Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the target genes. Finally, mRNA-miRNA networks were constructed. A total of 442 co-expressed DEGs were obtained through differential analysis, of which 353 expressed up-regulated genes and 89 expressed down-regulated genes. The up-regulated DEGs were involved in 742 GOs and 10 KEGG enrichment results, mainly associated with lipid metabolism-related pathways, TGF-β receptor signaling pathway, lipid transport, and PPAR signaling pathway. A total of 4 target genes (CREBBP, EP300, ME1, CD36) were identified. Analysis of subject operating characteristic curves indicated that CREBBP (AUC = 1), EP300 (AUC = 0.917), ME1 (AUC = 0.944) and CD36 (AUC = 1) may be candidate serum biomarkers for DPN. Conclusion: Diabetic peripheral neuropathy pathogenesis and progression is caused by multiple pathways, which also provides clinicians with potential therapeutic tools.


Identification of DEGs associated with diabetic peripheral neuropathy. Samples from GSE95849
were extracted and analysed separately using the limma package [version 3.42.2] to obtain differentially expressed genes (DEGs) between patients with diabetic peripheral neuropathy and healthy participants, and the results were de-duplicated. FDR was used to correct the q-values for multiple hypothesis testing, log2FC|> 1, p < 0.05 was statistically significant, and gene IDs were converted to gene symbols according to human genome GRCh38.93. Subsequently, to better understand the DEGs, the differentially expressed genes obtained were applied separately to the R package [version 3.3.3] "ggplot2" for DEmRNA mapping and "ComplexHeatmap [version 2.2.0]" for heat mapping 35 . Finally, the up-regulated DEmRNAs and down-regulated DEmRNAs obtained from the screening were compared with the DEmRNAs identified in the Comparative Toxicogenomics Database, 2021 update (http:// ctd. mdibl. org/) database as "Diabetic Neurology" 36  www.nature.com/scientificreports/ used as a keyword to search for genes directly related to diabetic nephropathy. Genes related to amino acid metabolism, glucose metabolism and lipid metabolism were downloaded from the GSEA database 26 (https:// www. gsea-msigdb. org/ gsea/ msigdb/ index. jsp), and overlapping DEGs with consistent up-and down-regulation of expression associated with diabetic neuropathy were taken. Ultimately, VennDiagram [version 3.6.3]", the "ggplot2 package [version 3.3.3]" was used to plot the Venn diagram.
GO enrichment and KEGG signalling pathway enrichment analysis. The above DEGs were extracted and the DAVID online database (https: //david.ncifcrf.gov/) was used to perform a Gene Ontology (GO) function enrichment analysis with Homo sapiens in the background, providing the required GO function enrichment data. The GO functional enrichment data are annotated and classified according to the functions of the genes: biological process (BP), cellular component (CC), molecular function (MF). At the meantime, the KEGG (Kyoto Encyclopedia of Genes and Genomes) was used for function enrichment analysis of signalling pathway, so as to discover biological pathways that may be involved [37][38][39] . We set the minimum gene to 10 and the maximum gene to 500, with p < 0.05 and FDR < 0.2 considered to be statistically significant, to screen for major enrichment functions and pathways of differential genes 40 . The clusterProfiler package [version 3.14.3] was used for enrichment analysis 41  Network analysis of protein-protein interaction (PPI) of common DEGs. We used the STRING database (https:// string-db. org/) to present and evaluate PPI networks 43 . The common DEGs screened in this study were imported into STRING, and the STRING analysis tool allowed further exploration of potential associations between these DEGs. The results of interaction node data with joint scores > 0.7 high confidence were imported into Cytoscape (version 3.8.2), and the protein interaction network was analysed for common differentially expressed genes, and visualisation and association analysis 40 . The top 20 genes in the PPI network were then tagged as hub genes using the degree algorithm of the CytoHubba plugin to filter the top 20 genes in key positions in the PPI network 44,45 . Links between all cluster pairs were shown using Spearman correlations, and pairwise correlations between clusters were visualised as chord plots in R (version 3.6.3) using the circlize package [version 0.4.12] 46 . GO and KEGG analyses were also performed on potential hub genes 40,41 .
Gene set enrichment analysis (GSEA). To explore biological signalling pathways and key genes, we used the clusterProfiler package [version 3.14.3] 41 to perform gene set enrichment analysis (GSEA) 47 . The MSigDB Collections gene set database was used as the reference gene set for the species: Homo sapiens, with c2.cp.v7.2.symbols.gmt [Curated]. The corrected normalized enrichment score |NES|> 1, False discovery rate (FDR) < 0.25 and p.adjusted < 0.05 conditions were considered significantly enriched, and relevant enrichment pathways and core genes that play a key role in these enrichment pathways were selected out.
Intergroup differential expression of diabetic peripheral neuropathy hub genes. Statistical analyses of potential pivotal genes were performed using the R package (version 3.6.3), and differences between DPN and normal participant groups were determined using the student t-test and Weltch's t' test. Data were tested for normality and chi-squared, and t-tests were used if the distribution was close to normal (p > 0.05). If the variance of the observed variables in the two groups was equal (p > 0.05), the independent samples t-test is used, and if the variance of the observed variables in the two groups was not statistically equal (p < 0.05), the Weltch t' test is applied. Finally, a combined plot of point, box and violin plots was visualised using ggplot2 [version 3.3.3], with the significance mark: ns, p ≥ 0.05; *, p < 0.05; **, p < 0.01; ***, p < 0.001.

Diagnostic validation of target genes.
In the gene sets GSE95849 and GSE24290, ROC curve analysis was performed using the pROC package to determine the sensitivity and specificity of each of the 16 target genes, the ROC-related information and data for the predictor variables at their respective cut-off values, and to assess the accuracy of the gene for the diagnosis of DPN. The results were quantified with the area under the ROC curve (AUC) and genes with an AUC > of 0.6 were selected as diagnostic genes, again visualised using ggplot2.
Construction of mRNA-miRNA regulatory networks and prediction of key miRNA. The miR-Walk database 48 (http:// mirwa lk. umm. uni-heide lberg. de/) Jan/2021 -new update was used for interactions between differentially expressed mRNAs and miRNAs. Based on this, prediction of miRNAs was performed on the miRTarBase database and miRDB database 46 . Candidate miRNAs were obtained from the intersection of the 3 databases 47,48 and Cytoscape 3.8.2 was used to visualize the regulatory network from which the important miRNAs and mRNAs were screened out.

Results
Screening for pivotal genes in diabetic peripheral neuropathy. In screening the DEGs, a total of 6 DPN samples and 6 control samples were included in the GEO dataset GSE95849, and this dataset was normalised. A principal component analysis (PCA) of GSE95849 was conducted to demonstrate clustering using scatter plots. Each point in the scatter plot represents one sample each, with 38.2% for PC1 and 16% for PC2, and the plot shows significant differences between the groupings (Fig. 1A). The volcano plot shows that a total of 16,405 differentially expressed genes were identified, of which 9822 were up-regulated and 6583 were down-regulated www.nature.com/scientificreports/ (Fig. 1C). The heat map shows the top 20 up-regulated genes and the top 20 down-regulated genes (Fig. 1B). Finally, differential analysis were performed by DESeq2, which determined the log2 fold changes, Wald test p-values and adjusted p-value (FDR) by the Benjamini-Hochberg procedure. Significantly regulated genes were defined as log2FC > 1 or < -1, and FDR < 0.5. According to the screening condition |log2FC|> 1, p < 0.05, 4601 genes with up-regulated differences in expression and 2034 genes with down-regulated differences in expression were screened from the GSE9589 dataset. The above differential genes were screened together with 23,971 genes related to diabetic neuropathy from the CTD database and 1404 key genes of glucose metabolism, lipid metabolism and amino acid metabolism from the GSEA database to create a Venn diagram. The results showed that the expression of 442 overlapping DEGs was generally different, with 353 genes being up-regulated and 89 genes being down-regulated (Fig. 1D, E). Because the screened DEGs would contain genes that were inconsistently up-and down-regulated, direct bioinformatic analysis of genes and pathways associated with diabetic peripheral www.nature.com/scientificreports/ neuropathy disease would confound the effect of false-positive co-expressed genes. To exclude this confounding factor, and to screen for genes that can be used as predictive targets for clinical diagnosis and prognosis, compared with those of normal healthy individuals, up-regulated genes are more clinically feasible to apply and study. Moreover, up-regulated genes are more practical from a diagnostic or therapeutic view point, we thereafter focused our attention on the up-regulated genes.We therefore selected only genes with up-regulated expression among the co-differential genes for analysis.

Results of GO and KEGG enrichment analysis. In DAVID-based GO biological process and KEGG
signalling pathway enrichment annotation analysis on 353 overlapping gene DEGs, meeting the conditions of p.adj < 0.05 & qvalue < 0.2 for significant enrichment, there were 563 BP, 59 CC, 120 MF and 61 KEGG. After arranging them according to the FDR values from smallest to largest, they were visualized as in (Fig. 2A). These DEGs were significantly enriched in small molecule catabolic process, carboxylic acid and organic acid biosynthetic and catabolic process, glycerolipid metabolic and biosynthetic process, phospholipid biosynthetic process, phospholipid metabolic process, glycerophospholipid metabolic process, fatty acid metabolic process, coenzyme metabolic process and other processes (Fig. 2C). In the KEGG enrichment analysis, DEGs were involved in insulin resistance, MAPK signaling pathway, cAMP signaling pathway, FoxO signaling pathway, TNF-beta signaling pathway, Adipocytokine signaling pathway, Growth hormone synthesis, secretion and action, Cell cycle, Glycerophospholipid metabolism, Valine, Leucine and Isoleucine degradation (Val) , Carbon metabolism, Fatty acid metabolism, PPAR signaling pathway, Sphingolipid metabolism, Peroxisome metabolism Peroxisome, Fatty acid degradation, Glycolysis / Gluconeogenesis, Glucagon signaling pathway and other metabolic processes (Fig. 2B,D).

Construction of PPI networks and identification of hub genes.
To understand the interactions between upregulated DEGs, a PPI network was constructed using STRING for co-expressing DEGs (Fig. 3A), and the results were then imported into Cytoscape v.3.8.2 software, and the genes in this network were ranked according to their degree values using the cytoHubba plugin to identify the top 20 hub genes with the highest degree. www.nature.com/scientificreports/ With an App called cytoHubba in Cytoscape software (version 3.8.2), we calculated the connectivity degree of each gene and selected the top 20 most central genes in the PPI network。These hub genes were ACOX1, RXRA, CREBBP, PPARA, EP300, HELZ2, NCOA2, ME1, HSD17B4, ACSL1, CHD9, CD36, ACOX3, ALDH6A1, HMGCL, DLAT, DBT, PGM1, PTEN, TALDO1 (Fig. 3B). Correlation analysis was also performed for 20 pivotal genes, and the chord plot showed that 16 of them were positively correlated (Fig. 3C). Subsequently, GO and KEGG enrichment analyses were performed on the 20 pivotal genes. The results showed that most of the 20 pivotal genes were enriched in the regulatory processes of lipid metabolism, amino acid metabolism, PPAR signalling pathway, cAMP signalling pathway, fatty acid metabolism and glucagon signalling pathway related to the development and progression of diabetic peripheral neuropathy, as shown in Figure (  www.nature.com/scientificreports/ at an overall level using the Molecular Signatures Database through GSEA software. Compared with genes of normal human participants, a total of 16,405 genes were regulated, which were enriched using the clusterProfiler R package to analyse and explore potential functional pathways 38 . 111 gene sets were significantly enriched at a False discovery rate (FDR) < 0.25, p.adjusted < 0.05 and among the top20 pivotal genes, while PGM1, ALDH6A1, DBT, HELZ2 were not significantly enriched. The final screening of 16 pivotal genes that play a key role in the enrichment pathway associated with the onset and progression of diabetic peripheral neuropathy: ACOX1, RXRA, CREBBP, PPARA, EP300, NCOA2, ME1, HSD17B4, ACSL1, CHD9, CD36, ACOX3, HMGCL, DLAT, PTEN, and TALDO1. GSEA results showed that the most enriched biological processes were mainly the metabolic processes of linolenic acid, fatty acid oxidation using acyl coenzyme a oxidase, monocarboxylic acid transport, coenzyme metabolic processes, regulation of lipid metabolic processes, and peroxisomal substrates (Fig. 4); CREBBP, EP300, ME1, and CD34 were also specifically involved in REACTOME_ METABOLISM_OF_LIPIDS, REACTOME_NEUTROPHIL_DEGRANULATION, WP_VEGFA/VEGFR2_SIGNALING_PATHWAY, the REACTOME_TOLL_LIKE_RECEPTOR_CASCADES, PID_CMYB_PATHWAY, BIOCARTA_PPARA_PATH-WAY , REACTOME_CELL_CYCLE, REACTOME_REGULATION_OF_TLR_BY_ENDOGENOUS_LIGAND and other biological processes (Fig. 5 ).
Expression profile and screening identification of target genes associated with diabetic peripheral neuropathy. The results of detecting the expression of the screened target genes using GSE95849 showed that the expression of 16 diabetic peripheral neuropathy-related pivotal genes (ACOX1, RXRA, CREBBP, PPARA, EP300, NCOA2, ME1, HSD17B4, ACSL1 , CHD9, CD36, ACOX3, HMGCL, DLAT PTEN, TALDO1) were consistent with the predicted expression of genes between healthy individuals, and the  www.nature.com/scientificreports/ DPN group were higher than the Control group and the differences were statistically significant (Fig. 6). The GSE95849 was used to screen and identify target genes. To further assess the diagnostic value of target genes in diabetic peripheral neuropathy, ROC curves were established with area under the ROC curve values between 0.5 and 1. The closer the AUC was to 1, the better the diagnosis. AUC between 0.5 and 0.7 had low accuracy, AUC between 0.7 and 0.9 had some accuracy. AUC above 0.9 has a high accuracy. In the GSE95849 dataset, the variable CREBBP had high accuracy in predicting outcome in normal patients and patients with diabetic peripheral neuropathy (AUC = 1.000, CI = 1.000-1.000); the variable EP300 (AUC = 0.917, CI = 0.738-1.000); the variable ME1 (AUC = 0.944, CI = 0.816-1.000); and variable CD36 (AUC = 1.000, CI = 1.000-1.000) all had high accuracy in predictive power ( Fig. 7 A-D). We also used the GSE24290 database for validation, which included 18 patients with progressive diabetic peripheral neuropathy and 17 patients with non-progressive diabetic peripheral neuropathy, to create validation ROC curves. The results showed that GSE24290 confirmed CREBBP, EP300, ME1 and CD36 were equally important diagnostically. The predictive power of the variables CREBBP (AUC = 0.605, CI = 0.409-0.800); EP300 (AUC = 0.601, CI = 0.402-0.800); ME1 (AUC = 0.647, CI = 0.456-0.838); CD36 (AUC = 0.663, CI = 0.477-0.850), was consistent with the predictions in GSE95849 (Fig. 7 E-H). Subsequently, in combination with the results of KEGG enrichment analysis, among the three key genes with high diagnostic value, CREBBP and EP300 were mainly involved in the Glucagon signaling pathway, HIF-1 signaling pathway, and Thyroid hormone signaling pathway. CD36 is mainly involved in insulin resistance, AMPK signaling pathway, Adipocytokine signaling pathway and fat digestion and absorption. (Fig. 3F, G).

Results of network construction for mRNA and miRNA.
To further evaluate the potential of circulating miRNAs as markers of diabetic peripheral neuropathy to screen for important miRNAs and mRNAs, we used the miRWalk database to predict 16 Hub genes (ACOX1, RXRA, CREBBP, PPARA, EP300, NCOA2, ME1, HSD17B4, ACSL1 , CHD9 CD36, ACOX3, HMGCL, DLAT, PTEN, TALDO1) for target miRNAs. Meanwhile, in combination with the miRDB database, 200 target miRNAs in four specifically expressed target genes were obtained by screening and 400 mRNA-miRNA pairs were determined. Based on the predicted results, a visual mRNA-miRNA network consisting of 68 nodes and 90 edges was constructed by Cytoscape . There were 42 miRNAs regulating CREBBP, 10 miRNAs regulating EP300,6 miRNAs regulating SRXN1 and 32 miRNAs regulating CD36. We finally used the miRTarBase database for overlay validation, which is a database that integrates microRNA targets that have been experimentally validated. Three mRNAs were obtained: CREBBP, EP300 and CD36, corresponding to three miRNAs: hsa-miR-5193, hsa-miR-3173-3p and hsa-miR-7151-3p (Fig. 8). www.nature.com/scientificreports/

Discussion
In recent years, there has been an increasing amount of research into the diagnosis and treatment of diabetic peripheral neuropathy (DPN), but due to limited understanding of the pathogenesis of DPN and the lack of specific investigational drugs, patients with DPN have a rapidly progressive disease and still have a poor prognosis.
There is now some understanding of the pathogenesis of DPN and studies have shown that lipid metabolism, glucose metabolism, β-cell dysfunction, insulin resistance, mitochondrial damage, microvascular damage, microcirculatory disorders and ischaemia and hypoxia play an important role in the development and progression of DPN. However, a multifactorial aetiology involving metabolic and vascular factors remains controversial. In addition to glucose metabolism, other components of the metabolic syndrome may also play a role in the onset and progression of DPN. Of these, dyslipidemia is most closely associated with diabetic neuropathy. In this study, we screened 353 co-expressed DEGs by analyzing the GSE95849 microarray dataset of diabetic peripheral neuropathy in the GEO database, constructed a PPI network, screened 20 hub genes, combined with GSEA, GO/KEGG enrichment analysis and correlation analysis, and performed joint validation using the GSE24290 dataset. Four target genes, namely CREBBP, EP300, ME1 and CD36, were finally obtained. 200 target miRNAs and 400 mRNA-miRNA pairs were then screened by miRWalk for miRNAs associated with the four target genes, which were experimentally validated through miRTarBase database and miRDB database. The final 3 mRNAs were obtained after database overlap of miRNA target genes: CREBBP, EP300, CD36, corresponding to 3 miRNAs: hsa-miR-5193, hsa-miR-3173-3p, hsa-miR-7151-3p. In this study we found that the enriched GO classes were macrophage-derived processes such as foam cell differentiation, regulation of lipid metabolism, reduced oxygen content, positive regulation of the TGF-β receptor signalling pathway, positive regulation of Notch receptor target transcription, cellular carbohydrate metabolism, glucose metabolism, response to hypoxia, lipid transport, fatty acid metabolism, and coenzyme metabolism. In the KEGG enrichment analysis, DEGs were mainly involved in metabolic processes such as PPAR signalling pathway, CAMP signalling pathway, insulin resistance, and carbon metabolism. Among them, TGF-β is a pleiotropic cytokine, which is involved in inflammatory response. After neurological injury, TGF-β regulates the behaviour of neurons and glial cells, thus mediating the regenerative process 49 . Therefore, the upregulation of TGF-β signalling may be a pathological response to www.nature.com/scientificreports/ neurological injury. Lipoic acid is a common antioxidant for the treatment of DPN, while TGF-β increases lipid peroxidation and lipoic acid decreases TGF-β expression 50 . Lipoic acid reduces TGF-β expression. In our study, we combined KEGGF enrichment analysis and GSEA analysis to identify potential biomarkers and biological pathways in DPN, which ultimately yielded different results. Through screening of key genes and reviewing the literature, this study suggests that CREBBP, EP300, CD36 may be key genes for DPN occurrence. The results of this study show that CREBBP, EP300, CD36 are also important for the diagnosis of DPN, the pathogenesis of diabetic peripheral neuropathy is caused by the interaction of multiple pathways, and the pathogenesis is complex, requiring a comprehensive multi-target-multi-pathway analysis. P300 (EP300) and CBP (CREBBP) have at least 315 different cellular and viral interacting proteins and are considered to be the most tightly connected coactivators in the mammalian protein-protein interaction network 51,52 . Although there are two separate genes encoding CBP and p300, they share 61% sequence homology and are often referred to as p300/CBP 53 . p300 and CBP are transcriptional co-activators with histone acetyltransferase activity. A variety of B-cell transcription factors can recruit p300/CBP, and thus coactivators are important for B-cell function and health. It has been shown that competition for cellular transcription factors because of binding of restricted p300/CBP is an important regulator of transcription. So when this micro-competitive www.nature.com/scientificreports/ regulation is disrupted, it causes many diseases 54 . It is well documented that p300/CBP affects lipid metabolism in different tissues and cells. Furthermore, in mouse models of obesity and type 2 diabetes, high p300/CBP HAT activity is associated with ChREBP hyperacetylation and hepatic steatosis 55 . In our study, p300(EP300) and CBP(CREBBP) expression levels were upregulated in DNP patients, but no studies on p300(EP300), CBP(CREBBP) and DPN have been identified. The present study is the first to focus on the role of p300(EP300) and CBP(CREBBP) in DPN, and we would like to know whether p300(EP300) and CBP(CREBBP) affect DPN in DPN by influencing lipid metabolism toxicity, or by inflammatory processes induced by inflammatory stimuli. In any case, the exact mechanisms need to be further investigated.No studies have been conducted on P300 (EP300) and CBP (CREBBP) in the context of DPN. Relevant study on P300 (EP300) and CBP (CREBBP) and diabetic complications is that EP300 also play pro-apoptotic roles in neuron 56,57 . CD36 is a key mediator of ox-LDL uptake by macrophages and has received much attention. CD36 regulates a variety of physiological and pathological processes, including FA transport and lipid metabolism, angiogenesis, adhesion, inflammation, cardiomyopathy, diabetes and atherosclerosis. CD36 independently binds and recognises a variety of exogenous or endogenous ligands, including those found in pathogenic or pathogen-infected cells, apoptotic cells, long-chain fatty acids (LCFA), modified low-density lipoproteins (LDL) and high-density lipoproteins (HDL) 58 . Patients with CD36 deficiency or CD36 gene polymorphism often present with postprandial hyperlipidaemia and high levels of plasma apoB48, triglycerides, FA and celiac (CM) residues 59,60 . These observations suggest a role for CD36 in hyperlipidemia. These observations suggest the importance of CD36 in hyperlipidaemia and associated atherosclerosis. CD36 is involved in multiple processes of lipid metabolism, including dietary lipid intake, lipoprotein production and transport, lipid utilisation, storage and lipolysis, which is consistent with our sample selection and grouping and bioinformatic predictions. It's worth noticing that CD36 has been reported in several metabolic diseases, but there is currently no literature suggesting a relationship between CD36 and DPN. Our study suggests that this may be a novel prognostic factor in diabetic peripheral neuropathy and further studies are needed to investigate the mechanism of its role in DPN.CD36 facilitates the transport of free fatty acids across the cell membrane in adipocytes. Downregulation of CD36 in progressive DPN may reduce lipid uptake and affect myelin formation, but may exert a protective effect 61 .
CD36 is implicated in the initiation of Peripheral nerve inflammation. The upregulations.of CD36 and MAPK signaling pathway genes (TNF-α, IL-1a and TGF-β1) are closely associated with the nerves of BKS db/db mice and the some studies suggest a CD36-mediated inflammatory response 62 . Moreover, it is suspected that CD36 also modulates energy homeostasis-related signaling pathways, such as AMPK and PPAR pathways, changing the glucose and lipid metabolism in diabetic peripheral nerves 63 . Dyslipidaemia was shown to predispose to the dysregulation of lipid metabolism in peripheral nerves, correlated with upregulation of CD36 and diacylglycerol acyltransferase 2. Saturated FAs are incorporated into TAGs, which initiate nerve injury 64 .
At the same time, due to the widespread use of bioinformatics for gene chip analysis methods, we offer reasonable speculation on the reasons for the inconsistent expression levels of mi-RNAs in the development of diabetic peripheral neuropathy. The specific mechanisms of mi-RNA interactions with cytokines and their involvement in signalling pathways in disease progression require more exploration and experimentation. Similarly, www.nature.com/scientificreports/ the microRNAs identified in our current study, miRNA5193, miRNA3173 and miRNA7151, are associated with disease progression in diabetic peripheral neuropathy, but their specific targets of action in clinical disease development and related clues still need further investigation. The current study found that miRNA5193 down-regulates TR1M11 expression in prostate cancer 65 .miR-5193 is an essential suppressor of ovarian cancer development and an important downstream regulator of FUT1 carcinogenesis in ovarian cancer 66 . In the future, miR-5193 may play an important role in the inhibition of HBV replication 67 . Meanwhile, studies have shown that 68 downregulation of SNHG3 expression suppressed the malignant phenotype of cholangiocarcinoma cells through the miR-3173-5p/ERG axis 69 . Recent studies have shown that 70 CASC15 can act as an endogenous miRNA sponge to uptake and downregulate miR-7151-5p, thereby preventing the inhibition of WNT7A during papillary thyroid cancer progression. Our study has some limitations. Firstly, this study was based on a bioinformatics analysis of transcriptome profiles from public databases, which may differ from the actual situation. Only one dataset was used for screening and experimental validation of some miRNA-mRNA pairs was lacking. Secondly, although the four genes screened have previously been reported to mediate diabetes and metabolism-related diseases, there is no direct evidence that they regulate the onset, progression and prognosis of diabetic peripheral neuropathy. Therefore, further experimental evidence is needed to validate the specific regulatory functions of these genes in diabetic peripheral neuropathy. Finally, prospective clinical trial cohorts and more in-depth molecular biology experiments need to be designed and conducted to further validate the mechanism of action of these four related genes in the development of diabetic peripheral neuropathy.
The necessity and clinical significance of this study lies in that, given the current state of medical development, there is no cure for diabetes, thus it is particularly important to actively prevent diabetes complications in people who have not yet developed them, to improve DM patients' lifestyle, to improve their quality of living and to www.nature.com/scientificreports/ reduce disability and mortality. Finding the right target for treatment, giving individualised treatment plans and more targeted treatment can reduce the economic pressure on patients, families and even the society as a whole. In summary, the candidate genes CREBBP, EP300, CD36, miRNA5193, miRNA3173 and miRNA7151, which were screened based on bioinformatics analysis, can influence the process of diabetic peripheral neuropathy through lipid metabolism, TGF-β receptor signaling pathway, lipid transport and PPAR signaling pathway. They may play an important role in the clinical disease progression of diabetic peripheral neuropathy, providing meaningful research clues and directions for clinical prognosis determination and treatment.

Data availability
The sequencing data used to support the findings of this study have been deposited in the GEO repository (GSE95849 and GSE24290). The datasets generated and/or analysed during the current study are available in the NCBI repository, https:// www. ncbi. nlm. nih. gov/ geo/ query/ acc. cgi? acc= GSE95 849, https:// www. ncbi. nlm. nih. gov/ geo/ query/ acc. cgi? acc= GSE24 290.